]]>I have more to say: Neuroscientists Debate A Simple Question. NPR piece on working memory.http://ekmillerlab.mit.edu/2018/11/05/i-have-more-to-say-neuroscientists-debate-a-simple-question-npr-piece-on-working-memory/
Mon, 05 Nov 2018 17:20:28 +0000http://ekmillerlab.mit.edu/?p=6778Check out this National Public Radio piece on the debate about our new model of working memory:Neuroscientists Debate A Simple Question: How Does The Brain Store A Phone Number? I have a few follow-up points.

1. In the piece, Christos Constantinidis says: “The problem with the theory is that so far there has been no experimental evidence linking this critical variable with behavior,”

2. Christos also says: Miller’s contention that working memory is linked to long-term memory seems at odds with doctors’ experience with patients whose brains have been injured.

This totally misses the point. Working memory can exploit some of the same mechanisms as long-term memory (synaptic weight changes) while at the same time rely on different brain areas. The fact that you can have brain damage that disrupts working memory without disrupting long-term memory is completely irrelevant.

3. Most importantly, the people who support the old model of persistent activity have not done the crucial test. All the evidence for the old model of persistent activity averages activity across trials. You cannot do this. Averaging creates an illusion of persistence. You must examine activity on single trials. Unless you do that, you are not addressing the issue.

If you want to read about our new working memory, check out this paper:
Miller, E.K., Lundqvist, L., and Bastos, A.M. (2018) Working Memory 2.0 Neuron, DOI:https://doi.org/10.1016/j.neuron.2018.09.023 View PDF

]]>Working Memory 2.0http://ekmillerlab.mit.edu/2018/10/24/working-memory-2-0/
Wed, 24 Oct 2018 15:14:35 +0000http://ekmillerlab.mit.edu/?p=6757Our new paper describing a new model of working memory. Actually, not so much a new model as an update to the classic model. The classic model posited that we hold thoughts “in mind” (i.e., in working memory) via the persistent spiking of neurons. That is not wrong. It is right to a certain level of approximation. There is little doubt that spikes help maintain working memories. However, a closer examination revealed that there is much more going on than persistent spiking.

It is important to keep in mind (pun intended) that virtually all evidence for persistent spiking comes from experiments that averaged neural activity across trials. The assumption was that averaging boosts signal and decreases noise (“noise” meaning changes in activity from trial to trial). But what if that noise was not noise but real neural dynamics? We don’t always think about the same thing in the same way. Averaging assumes we do.

With this in mind, we and others have been leveraging multiple-electrode recording to examine neural activity in “real time” (on individual trials). This has revealed that working memory-related spiking occurs in sparse, coordinated bursts of activity. It also revealed oscillatory dynamics between brain waves in two frequency bands, beta and gamma. Gamma seems to act as a carrier wave that maintains the contents of working memory. Beta seems carry the top-down control signals that allow us to exert volitional control over working memory.

Here’s the abstract:
Working memory is the fundamental function by which we break free from reflexive input-output reactions to gain control over our own thoughts. It has two types of mechanisms: online maintenance of information and its volitional or executive control. Classic models proposed persistent spiking for maintenance but have not explicitly addressed executive control. We review recent theoretical and empirical studies that suggest updates and additions to the classic model. Synaptic weight changes between sparse bursts of spiking strengthen working memory maintenance. Executive control acts via interplay between network oscillations in gamma (30–100 Hz) in superficial cortical layers (layers 2 and 3) and alpha and beta (10–30 Hz) in deep cortical layers (layers 5 and 6). Deep-layer alpha and beta are associated with top-down information and inhibition. It regulates the flow of bottom-up sensory information associated with superficial layer gamma. We propose that interactions between different rhythms in distinct cortical layers underlie working memory maintenance and its volitional control.

]]>Modulation of Beta Bursts in the Subthalamic Nucleus Predicts Motor Performancehttp://ekmillerlab.mit.edu/2018/10/18/modulation-of-beta-bursts-in-the-subthalamic-nucleus-predicts-motor-performance/
Thu, 18 Oct 2018 15:23:26 +0000http://ekmillerlab.mit.edu/?p=6752A trial by trial analysis showed that beta bursts, as opposed to power averaged across trials, is a good predictor of variations in motor behavior.

Abstract

The problem of identifying functional connectivity from multiple time series data recorded in each of two or more brain areas arises in many neuroscientific investigations. For a single stationary time series in each of two brain areas statistical tools such as cross-correlation and Granger causality may be applied. On the other hand, to examine multivariate interactions at a single time point, canonical correlation, which finds the linear combinations of signals that maximize the correlation, may be used. We report here a new method that produces interpretations much like these standard techniques and, in addition, 1) extends the idea of canonical correlation to 3-way arrays (with dimensionality number of signals by number of time points by number of trials), 2) allows for nonstationarity, 3) also allows for nonlinearity, 4) scales well as the number of signals increases, and 5) captures predictive relationships, as is done with Granger causality. We demonstrate the effectiveness of the method through simulation studies and illustrate by analyzing local field potentials recorded from a behaving primate.

]]>Earl Miller wins George A. Miller Prize in Cognitive Neurosciencehttp://ekmillerlab.mit.edu/2018/10/15/earl-miller-wins-george-a-miller-prize-in-cognitive-neuroscience/
Mon, 15 Oct 2018 15:01:30 +0000http://ekmillerlab.mit.edu/?p=6746When MIT neuroscientist Earl Miller was in graduate school at Princeton, he was inspired by the lectures of George A. Miller, an influential psychologist who helped to spark the young student’s interest in working memory. Now, as the newly named 2019 recipient of the George A. Miller Prize in Cognitive Neuroscience, Earl Miller is set to deliver a lecture honoring his teacher at the annual meeting of the Cognitive Neuroscience Society in San Francisco in March.

]]>Decoding Cognitive Processes from Neural Ensembleshttp://ekmillerlab.mit.edu/2018/10/04/decoding-cognitive-processes-from-neural-ensembles/
Thu, 04 Oct 2018 15:18:31 +0000http://ekmillerlab.mit.edu/?p=6743Nice review from Miler Lab alumnus Joni Wallis arguing for the importance of single-trial analyses. Variability across trials may not be noise, it may be cognition. Joni argues that ensembles, not single neurons, are the fundamental unit in the brain. One needs to record from many neurons simultaneously to understand cognitive processes.

This is very consistent with our recent work:
Bastos, A.M., Loonis, R., Kornblith, S., Lundqvist, M., and Miller, E.K. (2018) Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory. Proceedings of the National Academy of Sciences. View PDF

]]>Dissociation of LFP Power and Tuning in the Frontal Cortex during Memory – sort ofhttp://ekmillerlab.mit.edu/2018/09/25/dissociation-of-lfp-power-and-tuning-in-the-frontal-cortex-during-memory-sort-of/
Tue, 25 Sep 2018 15:38:37 +0000http://ekmillerlab.mit.edu/?p=6729Holmes, C.D., Papadimitriou, C., Snyder, L.H.(2018) Dissociation of LFP Power and Tuning in the Frontal Cortex during Memory Journal of Neuroscience

Nice paper. Well done. But with a caveat. The authors show that absolute power is dissociated from neural tuning in spiking activity. From this, they conclude that “oscillatory activity by itself is likely not a substrate of memory” and “may be an epiphenomenon of a rate code in the circuit, rather than a direct substrate”.

Not quite. No one is claiming that absolute power alone carries specific information. Rather, it is *patterns of coherence* that carry information (e.g., Buschman et al., 2012; Salazar et al 2012; Antzoulatos and Miller, 2014). If so, there is no reason to think that information would be carried by absolute power. For example, two different patterns of coherence for two different items could have equal global power because it is the pattern, not the global power, that matters. In fact, we and others have shown that coherence and power can be dissociated (Buschman et al., 2012). Using absolute power as a proxy to argue against a functional role for oscillations is a “straw man” argument. It tests a hypothesis that does not reflect the state-of-the-art of thinking on this matter.

Another point: The reason they see “tuning” for contra vs ipsilateral targets in power is not because of stimulus tuning per se, it is because the right vs left visual hemifields are somewhat independent. See:
Buschman,T.J., Siegel, M., Roy, J.E. and Miller, E.K. (2011) Neural substrates of cognitive capacity limitations. Proceedings of the National Academy of Sciences. 108(27):11252-5. View PDF »

Abstract
In addition to the prefrontal cortex (PFC), the basal ganglia (BG) have been increasingly often reported to play a fundamental role in category learning, but the systems-level circuits of how both interact remain to be explored. We developed a novel neuro-computational model of category learning that particularly addresses the BG-PFC interplay. We propose that the BG bias PFC activity by removing the inhibition of cortico-thalamo-cortical loop and thereby provide a teaching signal to guide the acquisition of category representations in the cortico-cortical associations to the PFC. Our model replicates key behavioral and physiological data of macaque monkey learning a prototype distortion task from Antzoulatos and Miller (2011). Our simulations allowed us to gain a deeper insight into the observed drop of category selectivity in striatal neurons seen in the experimental data and in the model. The simulation results and a new analysis of the experimental data, based on the model’s predictions, show that the drop in category selectivity of the striatum emerges as the variability of responses in the striatum rises when confronting the BG with an increasingly larger number of stimuli to be classified. The neuro-computational model therefore provides new testable insights of systems-level brain circuits involved in category learning which may also be generalized to better understand other cortico-basal ganglia-cortical loops

]]>Phase-Locked Stimulation during Cortical Beta Oscillations Produces Bidirectional Synaptic Plasticity in Awake Monkeyshttp://ekmillerlab.mit.edu/2018/09/11/phase-locked-stimulation-during-cortical-beta-oscillations-produces-bidirectional-synaptic-plasticity-in-awake-monkeys-2/
Tue, 11 Sep 2018 19:58:06 +0000http://ekmillerlab.mit.edu/?p=6717Zanos et al show that beta oscillations play a role in short-term synaptic plasticity in primate neocortex that may explain the role of oscillations in attention, learning, and cortical reorganization.

]]>A Flexible Model of Working Memoryhttp://ekmillerlab.mit.edu/2018/09/11/a-flexible-model-of-working-memory/
Tue, 11 Sep 2018 15:41:53 +0000http://ekmillerlab.mit.edu/?p=6715Bouchacourt and Buschman describe a two-layer model of working memory. A sensory layer feeds into an unstructured layer of neurons with random connections (i.e., “mixed-selectivity” type neurons). It is flexible but interference between representations results in a capacity limit. Sounds like working memory to me.

Abstract:
Working memory (WM) is characterized by the ability to maintain stable representations over time; however, neural activity associated with WM maintenance can be highly dynamic. We explore whether complex population coding dynamics during WM relate to the intrinsic temporal properties of single neurons in lateral prefrontal cortex (lPFC), the frontal eye fields (FEF), and lateral intraparietal cortex (LIP) of two monkeys (Macaca mulatta). We find that cells with short timescales carry memory information relatively early during memory encoding in lPFC; whereas long-timescale cells play a greater role later during processing, dominating coding in the delay period. We also observe a link between functional connectivity at rest and the intrinsic timescale in FEF and LIP. Our results indicate that individual differences in the temporal processing capacity predict complex neuronal dynamics during WM, ranging from rapid dynamic encoding of stimuli to slower, but stable, maintenance of mnemonic information.

]]>Attention is rhythmic!http://ekmillerlab.mit.edu/2018/08/28/attention-is-rhythmic/
Tue, 28 Aug 2018 15:08:57 +0000http://ekmillerlab.mit.edu/?p=6696Two new, exciting papers in Neuron that “put the last nail(s) in the coffin of sustained attention.” They present compelling evidence that sustained attention is not sustained at all but fluctuates with theta rhythms and alpha/beta rhythms. This provides yet more evidence that the brain works by rhythmic switching between representations.

]]>Structuring of Abstract Working Memory Content by Fronto-parietal Synchrony in Primate Cortexhttp://ekmillerlab.mit.edu/2018/08/14/structuring-of-abstract-working-memory-content-by-fronto-parietal-synchrony-in-primate-cortex/
Tue, 14 Aug 2018 18:45:21 +0000http://ekmillerlab.mit.edu/?p=6683Super-cool paper by Andreas Nieder and crew. Frontal-parietal beta synchrony encodes the most recent numerical input. Theta synchrony distinguishes between different numerosities held in working memory. The spiking of mixed-selectivity neurons multiplexed both task-relevant and irrelevant stimuli but they were separated in different phases of theta oscillations. Powerful support that neural oscillations functionally organize spiking activty.

Also, the idea that synaptic weight changes help maintain working memories is not altogether new. Goldman-Rakic suggested such a mechanism. Her lab found that sparse firing in the PFC produces temporary changes in synaptic weights. Importantly, if neurons firing too fast, inhibitory mechanisms kick in and you don’t get the weight changes. See:

But, hey, don’t take our word for it: Look at memory delay activity on single trials and tell us what *you* see.

]]>Adaptive coding in the human brain: Distinct object features are encoded by overlapping voxels in frontoparietal cortexhttp://ekmillerlab.mit.edu/2018/07/31/adaptive-coding-in-the-human-brain-distinct-object-features-are-encoded-by-overlapping-voxels-in-frontoparietal-cortex/
Tue, 31 Jul 2018 16:53:07 +0000http://ekmillerlab.mit.edu/?p=6667More evidence for mixed-selectivity in the cortex. This time with voxels in the human brain.

]]>Visual and Category Representations Shaped by the Interaction Between Inferior Temporal and Prefrontal Corticeshttp://ekmillerlab.mit.edu/2018/06/20/visual-and-category-representations-shaped-by-the-interaction-between-inferior-temporal-and-prefrontal-cortices/
Wed, 20 Jun 2018 18:03:44 +0000http://ekmillerlab.mit.edu/?p=6647A computational model of visual categorization in cortex that has properties similar to our lab’s results. It must be true.